Proceedings ArticleDOI
Test Case Prioritization Technique Based on Genetic Algorithm
Wang Jun,Zhuang Yan,Jianyun Chen +2 more
- pp 173-175
TLDR
A genetic algorithm-based test case prioritization algorithm is designed and the genetic algorithm proposed software test case priorities algorithm is improved.Abstract:
With the rapid development of information technology, software testing, as a software quality assurance, is becoming more and more important. In the software life cycle, each time the code has changed need to be regression testing. The huge test case library makes running a full test case library being challenged. To this end, we designed a genetic algorithm-based test case prioritization algorithm and improved the genetic algorithm proposed software test case prioritization algorithm.read more
Citations
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Journal ArticleDOI
Test case prioritization approaches in regression testing: A systematic literature review
TL;DR: This review examines and classify the current test case prioritization approaches in TCP based on the articulated research questions and found that variations in the starting point of TCP process among the approaches provide a different timeline and benefit to project manager to choose which approaches suite with the project schedule and available resources.
Proceedings ArticleDOI
Test case prioritization using multi objective particle swarm optimizer
Manika Tyagi,Sona Malhotra +1 more
TL;DR: The proposed MOPSO approach is compared with other prioritization techniques such as No Ordering, Reverse Ordering and Random Ordering by calculating Average Percentage of fault detected (APFD) for each technique and it can be concluded that the proposed approach outperformed all techniques mentioned above.
Proceedings ArticleDOI
Test case prioritization techniques “an empirical study”
Neha Sharma,Sujata,G.N. Purohit +2 more
TL;DR: The comparison of metaheuristic genetic algorithm with other algorithms and proves the efficiency of genetic algorithm over the remaining ones is focused on.
Journal ArticleDOI
A Framework for Continuous Regression and Integration Testing in IoT Systems Based on Deep Learning and Search-Based Techniques
TL;DR: A scalable framework for continuous integration and regression testing in IoT-based systems (IoT-CIRTF) is proposed, based on IoT-related criteria for test case prioritization and selection, which provides an enhanced and efficient framework for continuously testing of IoT- based systems.
Journal ArticleDOI
Ants Optimization for Minimal Test Case Selection and Prioritization as to Reduce the Cost of Regression Testing
TL;DR: The main objective of this thesis is to solve the path problem: Means to find the shortest path and Resolve the time problem: means to minimize the time of finding shortest path.
References
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Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Journal ArticleDOI
Prioritizing test cases for regression testing
TL;DR: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal as discussed by the authors, such as rate of fault detection, a measure of how quickly faults are detected within the testing process.
Proceedings ArticleDOI
Prioritizing test cases for regression testing
TL;DR: Can prioritization techniques be effective when aimed at specific modified versions; what tradeoffs exist between fine granularity and coarse granularity prioritized techniques; and can the incorporation of measures of fault proneness into prioritization technique improve their effectiveness?
Journal ArticleDOI
Search Algorithms for Regression Test Case Prioritization
TL;DR: The paper addresses the problems of choice of fitness metric, characterization of landscape modality, and determination of the most suitable search technique to apply, and sheds light on the nature of the regression testing search space, indicating that it is multimodal.
Proceedings ArticleDOI
Pareto efficient multi-objective test case selection
Shin Yoo,Mark Harman +1 more
TL;DR: The concept of Pareto efficiency to test case selection takes multiple objectives such as code coverage, past fault-detection history and execution cost, and constructs a group of non-dominating, equivalently optimal test case subsets.